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Information-Theoretic Generalization Bounds for Stochastic Gradient Descent with Predictable Virtual Noise

arXiv:2605.00064v1 Announce Type: new Abstract: Information-theoretic generalization bounds analyze stochastic optimization by relating expected generalization error to the mutual information between

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InpaintSLat: Inpainting Structured 3D Latents via Initial Noise Optimization

DGX agent

arXiv:2605.00664v1 Announce Type: new Abstract: We present a training-free approach for controllable 3D inpainting based on initial noise optimization. In the structured 3D latent diffusion framework,

safetyarxiv-cs-cv

4 May 2026

Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy

arXiv:2510.16450v2 Announce Type: replace Abstract: Annotation-efficient segmentation of the numerous mitochondria instances from various electron microscopy (EM) images is highly valuable for biologi

Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale

arXiv:2605.00324v1 Announce Type: cross Abstract: Large-scale ranking systems depend on thousands of features derived from user behavior across multiple time horizons. Typically requires model retrain

safetyarxiv-cs-lg

InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information

arXiv:2508.07630v2 Announce Type: replace Abstract: We introduce InterChart, a diagnostic benchmark that evaluates how well vision-language models (VLMs) reason across multiple related charts, a task

Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision-Language Models

arXiv:2605.00591v1 Announce Type: new Abstract: Contrastive vision-language models like CLIP exhibit remarkable zero-shot generalization. However, prompt tuning remains highly sensitive to label noise

Introducing WARM-VR: Benchmark Dataset for Multimodal Wearable Affect Recognition in Virtual Reality

model-releasesarxiv-cs-lg

arXiv:2605.00184v1 Announce Type: new Abstract: With the growing integration of human-computer interaction into everyday life, advances in machine learning have enabled systems to better perceive and

Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus

arXiv:2605.00618v1 Announce Type: new Abstract: We investigate the extent to which cosine similarity between paragraph embeddings is invariant under machine translation, using the Manifesto Corpus of

It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models

arXiv:2601.00090v2 Announce Type: replace Abstract: Contemporary text-to-image models exhibit a surprising degree of mode collapse, as can be seen when sampling several images given the same text prom

Jailbreaking Vision-Language Models Through the Visual Modality

arXiv:2605.00583v1 Announce Type: new Abstract: The visual modality of vision-language models (VLMs) is an underexplored attack surface for bypassing safety alignment. We introduce four jailbreak atta

Jailbroken Frontier Models Retain Their Capabilities

model-releasesarxiv-cs-lg

arXiv:2605.00267v1 Announce Type: new Abstract: As language model safeguards become more robust, attackers are pushed toward developing increasingly complex jailbreaks. Prior work has found that this

Knowing When to Defer: Selective Prediction for Responsible Knowledge Tracing

arXiv:2509.21514v3 Announce Type: replace-cross Abstract: Research on Knowledge Tracing (KT) models traditionally focuses on improving predictive accuracy. However, responsible real-world deployment r

Knowing when to trust machine-learned interatomic potentials

arXiv:2605.00640v1 Announce Type: new Abstract: Prevailing machine-learned interatomic potential (MLIP) uncertainty-quantification methods rely on ensembles of independently trained backbones. These m

Koopman-Assisted Reinforcement Learning

arXiv:2403.02290v2 Announce Type: replace-cross Abstract: The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman equation, are ubiquitous in reinforcement learning and control theor

LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction

arXiv:2605.00501v1 Announce Type: new Abstract: In financial predictions, the performance of machine learning models is often assessed by Rank IC, which is the Spearman rank correlation between the mo

applicationsarxiv-cs-lg

LandSegmenter: Towards a Flexible Foundation Model for Land Use and Land Cover Mapping

arXiv:2511.08156v2 Announce Type: replace Abstract: Land Use and Land Cover (LULC) mapping is a fundamental task in Earth Observation (EO). However, current LULC models are typically developed for a s

Language-free Experience at Expo 2025 Osaka

arXiv:2605.00373v1 Announce Type: new Abstract: In line with the Global Communication Plan 2025, we have pursued the development of multilingual translation technologies to realize a language-barrier-

applicationsarxiv-cs-cl

Language Models Struggle to Use Representations Learned In-Context

arXiv:2602.04212v2 Announce Type: replace Abstract: Though large language models (LLMs) have enabled great success across a wide variety of tasks, they still appear to fall short of one of the loftier

applicationsarxiv-cs-cl

LASE: Language-Adversarial Speaker Encoding for Indic Cross-Script Identity Preservation

arXiv:2605.00777v1 Announce Type: cross Abstract: A speaker encoder used in multilingual voice cloning should treat the same speaker identically regardless of which script the audio was uttered in. Of

Last-Iterate Analyses of FTRL with the 1/2-Tsallis Entropy in Stochastic Bandits

arXiv:2510.22819v2 Announce Type: replace Abstract: The convergence analysis of online learning algorithms is central to machine learning theory, where the last-iterate convergence is particularly imp

Last-Iterate Convergence of General Parameterized Policies in Constrained MDPs

arXiv:2408.11513v2 Announce Type: replace Abstract: This paper focuses on learning a Constrained Markov Decision Process (CMDP) via general parameterized policies. We propose a Primal-Dual based Regul

safetyarxiv-cs-lg

Tutorials

Latent Generative Modeling of Random Fields from Limited Training Data

arXiv:2505.13007v2 Announce Type: replace Abstract: The ability to accurately model random fields plays a critical role in science and engineering for problems involving uncertain, spatially-varying q

tutorialsarxiv-cs-lg

Learn where to Click from Yourself: On-Policy Self-Distillation for GUI Grounding

arXiv:2605.00642v1 Announce Type: cross Abstract: Graphical User Interface (GUI) grounding maps natural language instructions to the visual coordinates of target elements and serves as a core capabili

safetyarxiv-cs-cv

Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labels

arXiv:2605.00718v1 Announce Type: new Abstract: Knee osteoarthritis (OA) assessment involves a natural but often underused label hierarchy: a coarse binary OA decision and a fine-grained Kellgren--Law

safetyarxiv-cs-cv

Tutorials

Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization

arXiv:2605.00130v1 Announce Type: new Abstract: Learning meaningful representations from medical time series (MedTS) such as ECG or EEG signals is a critical challenge. These signals are often high-di

tutorialsarxiv-cs-lg

Learning from Compressed CT: Feature Attention Style Transfer and Structured Factorized Projections for Resource-Efficient Medical Image Analysis

arXiv:2605.00448v1 Announce Type: new Abstract: The deployment of artificial intelligence in medical imaging is hindered by high computational complexity and resource-intensive processing of volumetri

Learning from Supervision with Semantic and Episodic Memory: A Reflective Approach to Agent Adaptation

arXiv:2510.19897v2 Announce Type: replace Abstract: We investigate how agents built on pretrained large language models (LLMs) can learn target classification functions from labeled examples without p

Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation

arXiv:2605.00051v1 Announce Type: new Abstract: Anticipating traffic accidents is a critical yet unresolved problem for autonomous driving, hindered by the inherent complexity of modeling interactions

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory

arXiv:2605.00702v1 Announce Type: new Abstract: Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving pre

safetyarxiv-cs-cl

Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels

arXiv:2412.00452v2 Announce Type: replace-cross Abstract: Conventioanl federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the fed

Learning Multimodal Energy-Based Model with Multimodal Variational Auto-Encoder via MCMC Revision

arXiv:2605.00644v1 Announce Type: new Abstract: Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. Howeve

Learning physically grounded traffic accident reconstruction from public accident reports

arXiv:2605.00050v1 Announce Type: cross Abstract: Traffic accidents are routinely documented in textual reports, yet physically grounded accident reconstruction remains difficult because detailed scen

safetyarxiv-cs-cv

Local Ai

Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries

arXiv:2605.00760v1 Announce Type: new Abstract: This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network based on the De

local-aiarxiv-cs-lg

Learning while Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies

arXiv:2605.00416v1 Announce Type: new Abstract: Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. De

safetyarxiv-cs-ro

Let ViT Speak: Generative Language-Image Pre-training

arXiv:2605.00809v1 Announce Type: new Abstract: In this paper, we present extbf{Gen}erative extbf{L}anguage-extbf{I}mage extbf{P}re-training (GenLIP), a minimalist generative pretraining framework for

Leveraging Vision-Language Models as Weak Annotators in Active Learning

arXiv:2605.00480v1 Announce Type: new Abstract: Active learning aims to reduce annotation cost by selectively querying informative samples for supervision under a limited labeling budget. In this work

Lightweight Domain Adaptation of a Large Language Model for Legal Assistance in the Indian Context

arXiv:2505.22003v2 Announce Type: replace Abstract: In India, access to legal assistance for the general public has been observed to have a critical gap, as many citizens are not able to take full adv

LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations

arXiv:2605.00434v1 Announce Type: new Abstract: Real-world multimodal learning is often hindered by missing modalities. While Incomplete Multimodal Learning (IML) has gained traction, existing methods

applicationsarxiv-cs-cv

Linking Behaviour and Perception to Evaluate Meaningful Human Control over Partially Automated Driving

arXiv:2605.00556v1 Announce Type: cross Abstract: Partial driving automation creates a tension: drivers remain legally responsible for vehicle behaviour, yet their active control is significantly redu

safetyarxiv-cs-ro

LLM DNA: Tracing Model Evolution via Functional Representations

arXiv:2509.24496v3 Announce Type: replace Abstract: The explosive growth of large language models (LLMs) has created a vast but opaque landscape: millions of models exist, yet their evolutionary relat

LLM-Oriented Information Retrieval: A Denoising-First Perspective

arXiv:2605.00505v1 Announce Type: cross Abstract: Modern information retrieval (IR) is no longer consumed primarily by humans but increasingly by large language models (LLMs) via retrieval-augmented g

Agents

Long-Horizon Model-Based Offline Reinforcement Learning Without Explicit Conservatism

arXiv:2512.04341v3 Announce Type: replace Abstract: Popular offline reinforcement learning (RL) methods rely on explicit conservatism, penalizing out-of-dataset actions or restricting rollout horizons

agentsarxiv-cs-lg

Lost in State Space: Probing Frozen Mamba Representations

arXiv:2605.00253v1 Announce Type: new Abstract: Mamba's recurrent state h_t is, by construction, a compressed summary of every token seen so far. This raises a tempting hypothesis: if we extract token

Local Ai

Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation

arXiv:2605.00244v1 Announce Type: cross Abstract: We introduce Lucid-XR, a generative data engine for creating diverse and realistic-looking multi-modal data to train real-world robotic systems. At th

local-aiarxiv-cs-cv

M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data

model-releasesarxiv-cs-lg

arXiv:2605.00398v1 Announce Type: new Abstract: Causal graph discovery for space-time systems is challenging in high-dimensional gridded data, which often has many more grid cells than temporal observ

Tutorials

MAEPose: Self-Supervised Spatiotemporal Learning for Human Pose Estimation on mmWave Video

arXiv:2605.00242v1 Announce Type: new Abstract: Millimetre-wave (mmWave) radar offers a more privacy-preserving alternative to RGB-based human pose estimation. However, existing methods typically rely

tutorialsarxiv-cs-cv

Make Your LVLM KV Cache More Lightweight

arXiv:2605.00789v1 Announce Type: new Abstract: Key-Value (KV) cache has become a de facto component of modern Large Vision-Language Models (LVLMs) for inference. While it enhances decoding efficiency

Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding

arXiv:2605.00342v1 Announce Type: new Abstract: Tree-based speculative decoding accelerates autoregressive generation by verifying multiple draft candidates in parallel, but this advantage weakens for